Refactor interpret module

4.interpret_model/README.md

@@ -1,14 +1,14 @@
 # 4. Interpret Model
 
-In this module, we interpret the final and shuffled baseline ML models.
+In this module, we interpret the ML models.
 
 After training the final and baseline models in [2.train_model](../2.train_model/), we load the coefficents of these models from [models/](../2.train_model/models).
 These coefficients are interpreted with the following diagrams:
 
 - We use [seaborn.heatmap](https://seaborn.pydata.org/generated/seaborn.heatmap.html) to display the coefficient values for each phenotypic class/feature.
 - We use [seaborn.clustermap](https://seaborn.pydata.org/generated/seaborn.clustermap.html) to display a hierarchically-clustered heatmap of coefficient values for each phenotypic class/feature
 - We use [seaborn.kedeplot](https://seaborn.pydata.org/generated/seaborn.kdeplot.html) to display a density plot of coeffiecient values for each phenotypic class.
-- We use [seaborn.barplot](https://seaborn.pydata.org/generated/seaborn.barplot.html) to display a bar plot of average coeffiecient values per phenotypic class and feature.
+- We use [seaborn.barplot](https://seaborn.pydata.org/generated/seaborn.barplot.html) to display a bar plot of average coeffiecient values per phenotypic class.
 
 ## Step 1: Interpret Model
 
@@ -27,6 +27,6 @@ bash interpret_model.sh
 
 ## Results
 
-Each model's interpretations can be found in [interpret_model.ipynb](interpret_model.ipynb).
+Each model's interpretations can be found in [interpret_model_coefficients.ipynb](interpret_model_coefficients.ipynb).
 
 **Note:** Intermediate `.tsv` data are stored in tidy format, a standardized data structure (see [Tidy Data](https://vita.had.co.nz/papers/tidy-data.pdf) by Hadley Wickham for more details).